Approximation algorithms for solving cost observable Markov decision processes Public Deposited

http://ir.library.oregonstate.edu/concern/technical_reports/1544bq53f

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  • "The specifi c problem addressed in this proposal is the development of good approximation algorithms for solving problems that have partial observability. The model we propose associates costs with obtaining information about the current state. We want to predict when and how much it is necessary to observe. We want to use our Cost Observable Markov Decision Process (COMDP) model to find good solutions for real-world problems ..."--Problem definition.
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  • description.provenance : Made available in DSpace on 2012-04-23T16:11:08Z (GMT). No. of bitstreams: 1 Approximation algorithms for solving cost observable Markov decision process.pdf: 237338 bytes, checksum: 0ba4b6c68b6faa9ff13db25ae295351d (MD5) Previous issue date: 1999
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2012-04-23T16:11:07Z (GMT) No. of bitstreams: 1 Approximation algorithms for solving cost observable Markov decision process.pdf: 237338 bytes, checksum: 0ba4b6c68b6faa9ff13db25ae295351d (MD5)
  • description.provenance : Submitted by Laura Wilson (laura.wilson@oregonstate.edu) on 2012-04-23T16:09:57Z No. of bitstreams: 1 Approximation algorithms for solving cost observable Markov decision process.pdf: 237338 bytes, checksum: 0ba4b6c68b6faa9ff13db25ae295351d (MD5)

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